Defining or arbitrating public policies is based on the answer to the following generic question: what will happen, what result will be achieved, if such actions and / or other actions are carried out?

3 different approaches

A first method is to examine whether there is already experience corresponding to the tested policy available, for example in another country, or from one region to another. The value of such a comparison should not be underestimated – in scientific terms one speaks of the state of the art – but often the comparison makes it possible to understand why a policy cannot succeed [i.e. the causes of failure can be transposed], it is not enough to establish a success plan [i.e. the recipe for success for one thing does not always transpose to another, in a different context].

A second method is to experiment, either at full scale or in a pilot study. This can lead to two or even three problems:
• relevance : assuming that the pilot experiment is favorable, will scaling-up remain successful ?
• ethical : experimentation must be reversible. In the case of failure of a tested policy, there must not be repercussions from any change,
• and finally, for certain areas, such as town planning, the process is long-term. It is not possible to wait for the conclusion of an experiment over 20 to 30 years to define public policy!

The third method is simulation, which is of course our recommendation. To do this, we construct a model to account for the behaviour of the system or even eco-system that we want to grasp, in order to deduce the consequences of decisions, public policies studied: Modelling - Simulation - Decision.

Do not be deceived. Simulation is in itself a skill, an expertise, that can be compared to computer science and applied mathematics. We cannot properly simulate large systems, regardless of how their size is accounted for by mastering various advanced techniques (equation solving algorithms, high performance computing, visualization, etc.).

Modelling is the heart of the subject, the lever to succeed. Different types of models, such as agent representations, physical and / or mathematical equations, statistical models, etc., can be envisaged at different scales. The challenge is then to create a multi-scale reconstruction to correctly represent and then simulate the whole.

Our experience is that in each area, over time, specialists focus on a given type of modelling, and exclude others. If the result is convincing, it is satisfactory. Anecdotally, the best model is the one that gives the best results! But the downside is that when faced with unconvincing results, for example when the context is changed, a discipline is no longer able to change its approach without being supplemented by other sources.

It is for this reason that Kannon MSD's has the vocation, using pluridisciplinarity or cross-fertilization between domains, to take on the approaches of modelling, simulation and decision-making for public policies : economy, town planning, support for technological development, ...